Deep learning neural networks are employed to analyze an image to determine if an object is present in the image. To do so, an image is pushed through a neural network trained to detect one or more objects. The trained neural network is configured to perform operations (e.g., matrix multiplications) using a set of learned parameters (resulting from a training phase) to determine if an object is detected. To process an entire image, these operations are performed by the trained neural network on each of the pixels to generate the object classification label, resulting in a significant computational expense (e.g., ten billion floating point operations). In this regard, every pixel of the image is analyzed to identify patterns in the image (e.g., shapes, sizes, colors, etc.) for purposes of classifying and labeling objects in the image.
In certain video processing systems, each frame in the video is processed by the trained neural network independently. In this architecture, after fully processing each frame, an analysis of what is occurring across the multiple frames is performed. As such, there are instances when a large amount of processing is performed with regard to two or more contiguous frames of video (i.e., processing each frame through the entire neural network), even though only a small amount of the data has changed from one frame to the next. Accordingly, processing of the multiple frames of a video in this manner results in computational inefficiencies.
While embodiments are described herein by way of example for several embodiments and illustrative drawings, those skilled in the art will recognize that the embodiments are not limited to the embodiments or drawings described. It should be understood that the drawings and detailed description thereto are not intended to limit embodiments to the particular form disclosed, but on the contrary, the intention is to cover all modifications, equivalents and alternatives falling within the spirit and scope as defined by the appended claims. The headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description or the claims. As used throughout this application, the word “may” is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words “include,” “including,” and “includes” mean including, but not limited to.